Band selection, aiming at screening representative spectral bands and eliminating redundant information, has long been a popular topic in hyperspectral imagery (HSI) processing, and has garnered a growing concern owing to the advancements in sparse representation techniques. Traditional sparsity-based methods are frequently impeded by the issue of inadequate or even unattainable training samples. Moreover, these approaches may fall short in thoroughly investigating the spatial structural information and spectral contextual information. To this end, this paper proposes a new unsupervised band selection scheme, namely, pseudo-label guided sparse regression with spatial and spectral regularization (PSR2BS), which embeds band selection into an unsupervised sparse regression model. Specifically, a pseudo-label matrix is jointly learned to serve as a discriminative cluster indicator, during which it guides the projection matrix in selecting informative bands. To leverage the spatial information within HSI, an image is segmented into different distinct homogeneous regions to generate representation samples, wherein the local structural information is also explored through spatial regularization. Furthermore, a spectral regularization term is introducing, making full use of prior information regarding similarity within spectral bands. To solve the proposed model, an effective and efficient iterative optimization algorithm is developed. Our extensive experiments on classification and anomaly detection across six real HSI datasets clearly demonstrate the superior performance of the proposed PSR2BS compared with state-of-the-art competitors.

An intelligent memory-based event-triggered impulsive control (METIC) scheme is proposed to address the stabilization problem for a class of nonlinear systems while accounting for exponential convergence, dynamic performance, and control frequency. The contribution of the scheme is the incorporation of weighted historical data into the triggering condition, using both fixed thresholds and adaptive thresholds based on Q-learning. By utilizing the system states at the two most recent triggering instants to construct the triggering condition, several exponential stability criteria are first established via an iterative approach. Then, in the general case in which additional historical states are included, a comparison system approach is employed to derive new stability conditions. Furthermore, to adaptively tune the event-triggering thresholds and improve system performance, a Q-learning-based optimization algorithm is developed, and a set of easily verifiable stability conditions is derived within the framework of switched system theory. For both fixed-threshold and adaptive-threshold cases, Zeno behavior is rigorously excluded through theoretical analysis. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method.

This study assesses the technical, operational, environmental, and economic feasibility of integrating alkaline water electrolysis (AEL) using on-site measured surplus electricity from two 20 MW natural-gas turbogenerators installed at a Central Processing Facility (CPF) in a Colombian oilfield. Unlike approaches based on modeled profiles, the analysis relies on more than 31,000 experimental records of gas consumption and active power, enabling an accurate characterization of the structural availability of energy surpluses under real operating conditions. A specialized industrial water treatment and purification company was consulted and provided with the physicochemical characterization results obtained from process water samples analyzed by an accredited laboratory. Based on these parameters, the technical supplier confirmed the feasibility of designing a multistage treatment train, including equalization, filtration, clarification, activated carbon, ultrafiltration, and reverse osmosis, capable of achieving final conductivities at or below 5 µS/cm. This water quality level is compatible with typical industrial alkaline electrolysis requirements and in line with technical specifications commonly aligned with ASTM and ISO standards for pressurized AEL systems. A strategic comparison between PEM and AEL technologies, supported by IFE/EFE matrices and sensitivity analyses, identified alkaline electrolysis as the optimal alternative under a stable electrical profile and capital expenditure constraints. Energy sizing for scenarios between 1.5 and 10 MW, assuming continuous 24 h operation and an average specific consumption of 50 kWh/kg H2, yields productions between 0.5 and 3.5 t H2/day, with electrical efficiencies above 70%. A 20-year financial analysis indicates a techno-economic threshold near 3 MW (NPV > 0; IRR > WACC), with optimal performance in the 6.5–10 MW range and payback periods between 2 and 4 years under internal valorization of the surplus electricity. From an environmental perspective, the produced hydrogen is classified as low-carbon rather than “green” due to its thermal origin; however, the integration improves the turbines’ operating regime and valorizes surplus electrical exergy that was previously unused, providing a replicable strategy for industrial assets with self-generation and treatable water availability.

A traditional goal of science and environmental communication, including climate communication, has been to encourage disinterested or uninformed audiences to pay more attention to the world around them and to shift disinterest and apathy toward positive engagement with nature and proenvironment lifestyles. We conducted an empirical investigation of audience responses to key aspects of the world scientists' "2024 State of the Climate Report: Perilous Times on Planet Earth," focusing on whether the language of this article manages to sway readers to rethink their attitudes toward climate change. Across many variations, the textual prompts we gave to readers did <i>not</i> overwhelmingly move the needle of public attitudes regarding climate change, suggesting that political affiliation and ideologies may be a much stronger indicator of public actions and attitudes than exposure to scientific information. Regarding climate change, we seem to be living in a time of information saturation and ideological entrenchment.

After laser powder bed fusion (LPBF) of an ultra-strong in situ TiC whisker reinforced β-Ti composite, this paper investigates the evolution of microstructure and mechanical properties in response to heat treatment at different temperatures. Using in depth nano-SEM and TEM analyses, it is shown that ageing at 400 °C rounds the whiskers, annihilates the strain fields and grows Mo segregated nano-cells, but without improving the ductility. In contrast, ageing at 600 °C enables the transformation of metastable β to a lamellar β + α, leading to a dual phase matrix embedding TiC particles. This is in such a manner that extra ageing at 600 °C coalesces the nano-lamellar α + β microstructure to form a coarser micro-lamellar α + β matrix. This microstructure achieves 66 % of the compressive deformation of Cp-Ti, and over 1400 MPa compressive strength after 1 h of ageing at 600 °C. Despite this success under compression, hard and stiff TiC particles may still cause large spherical fractured voids, severely limiting the plastic deformation under tension.

This paper studies a distributed online optimization problem over partially Free-In and Free-Out (FIFO) networks, in which a set of unfixed agents cooperate to minimize the sum of a group of time-varying functions over a time horizon. To be specific, the agents are divided into static agents and dynamic agents. The static agents are those who remain in the network during the whole time horizon, while the dynamic agents are allowed to join and leave the network freely. Based on the dual averaging technique, two novel distributed algorithms are developed to address the distributed optimization problem in such a dynamic environment. In the case where agents can distinguish whether their out-neighbors are dynamic agents or static agents, a weighting matrix based algorithm is developed. In the case where the identities of out-neighbors are unavailable, a gradient-storage based algorithm is developed, which has higher communication and local storage requirements than the weighting matrix based algorithm. By assuming that each dynamic agent has at least one in-neighbor and one out-neighbor who are static agents, the running average regret is shown to be upper bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{\mathit {T}}})$</tex-math></inline-formula> under suitable step-sizes, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit {T}$</tex-math></inline-formula> is the time horizon. A simulation study on binary classification is given to verify the effectiveness of the developed algorithms. In addition, a numerical example is used to highlight the advantages of the proposed methods over the distributed sub-gradient decent method.

Game theory has emerged as a fundamental framework for modeling and analyzing strategic interactions and decision-making among multiple agents, and has witnessed rapidly growing impact in cyber–physical systems over the past decade. Its integration with dynamic systems has driven major theoretical and technological advances in a wide range of applications, including smart grids, autonomous driving, robotic swarms, and networked control systems. In particular, distributed games in dynamic systems and their equilibrium learning mechanisms have attracted increasing attention due to their scalability, lightweight information exchange, and real-time implementability. This article provides a comprehensive survey of distributed games in dynamic systems, where agents interact only with local neighbors while collectively achieving global equilibrium and stability. First, the foundational theories of distributed dynamic games under three representative classes of systems: linear dynamic systems, nonlinear dynamic systems, and uncertain dynamic systems, are presented. Then, state-of-the-art distributed equilibrium learning and control methods are reviewed, including gradient-based dynamics, payoff-based learning, best-response dynamics, and learning-based approaches. To demonstrate the practical relevance and impact of distributed games in dynamic systems, representative application domains are discussed in detail. Finally, several promising future research directions are outlined, highlighting open challenges at the intersection of distributed games, learning, and dynamic systems.

Distributed optimization, as a key technology for collaborative intelligence in multiagent systems, has been widely applied in sensor networks, deep learning, and smart grids. Although numerous effective algorithms have been proposed, classical methods typically rely on idealized assumptions, such as accurate objective information, perfect communication channels, and trustworthy system environments. However, these assumptions are frequently violated in real-world applications. To bridge the gap between theory and practice, distributed optimization under information constraints has emerged as a research focus. This survey provides a systematic overview of recent advances in this field. We categorize information constraints based on their origin into three primary types: i) observational constraints, including stochastic objectives, online optimization, and zeroth-order methods; ii) communication constraints, such as random network topologies, delays, asynchronous updates, and communication-efficient strategies; and iii) system-level constraints, encompassing privacy preservation and Byzantine-resilient optimization. This survey reviews the research progress and challenges associated with each constraint category. Furthermore, we use two representative case studies to analyze the practical application of these algorithms and the origins of information constraints in real-world problems. Finally, we explore promising future research directions.